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ACMSE
2006
ACM
16 years 3 days ago
Reconstructing networks using co-temporal functions
Reconstructing networks from time series data is a difficult inverse problem. We apply two methods to this problem using co-temporal functions. Co-temporal functions capture mathe...
Edward E. Allen, Anthony Pecorella, Jacquelyn S. F...
CVPR
2008
IEEE
16 years 8 months ago
Enhanced biologically inspired model
It has been demonstrated by Serre et al. that the biologically inspired model (BIM) is effective for object recognition. It outperforms many state-of-the-art methods in challengin...
Yongzhen Huang, Kaiqi Huang, Liangsheng Wang, Dach...
160
Voted
FOCS
1997
IEEE
15 years 10 months ago
Randomized and Deterministic Algorithms for the Dimension of Algebraic Varieties
We prove old and new results on the complexity of computing the dimension of algebraic varieties. In particular, we show that this problem is NP-complete in the Blum-Shub-Smale mo...
Pascal Koiran
163
Voted
COMPUTING
2007
103views more  COMPUTING 2007»
15 years 6 months ago
An algebraic approach for H-matrix preconditioners
Hierarchical matrices (H-matrices) approximate matrices in a data-sparse way, and the approximate arithmetic for H-matrices is almost optimal. In this paper we present an algebrai...
S. Oliveira, F. Yang
NIPS
2000
15 years 7 months ago
Processing of Time Series by Neural Circuits with Biologically Realistic Synaptic Dynamics
Experimental data show that biological synapses behave quite differently from the symbolic synapses in common artificial neural network models. Biological synapses are dynamic, i....
Thomas Natschläger, Wolfgang Maass, Eduardo D...